Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

Sharma, Tapan; Shokeen, Vinod; Mathur, Sunil

doi:10.4018/ijssmet.2020040105

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…To obtain better noise immunity, we need to optimize and improve the traditional detection method with the basic operational steps: first, perform image preprocessing; then, perform edge detection; finally, enhance the obtained results and wait for the processing to obtain the desired results, but image preprocessing often blurs the image, which leads to loss of details [19]. The structural elements can be selected according to the essential features of the image and selected and adjusted for different situations to facilitate their application.…”

Section: Advances In Mathematical Physicsmentioning

confidence: 99%

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Chen

2021

Advances in Mathematical Physics

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In recent years, with the rapid development of image processing research, the study of nonstandard images has gradually become a research hotspot, for example, fabric images, remote sensing images, and gear images. Some of the remote sensing images have a complex background and low illumination compared with standard images and are easy to be mixed with noise during acquisition; some of the fabric images have rich texture information, which adds difficulty to the related processing, and are also easy to be mixed with noise during acquisition. In this paper, we propose a fractional-order adaptive P -Laplace equation image edge detection algorithm for the problem of image edge detection in which the edge and texture information of the image is lost. The algorithm can apply for the order adaptively to filter the noise according to the noise distribution of the image, and the adaptive diffusion factor is determined by both the fractional-order curvature and fractional-order gradient of the iso-illumination line and combined with the iterative approach to realize the fine-tuning of the noisy image. The experimental results demonstrate that the algorithm can remove the noise while preserving the texture and details of the image. A fractional-order partial differential equation image edge detection model with a fractional-order fidelity term is proposed for Gaussian noise. The model incorporates a fractional-order fidelity term because this fidelity term smoothes out the rougher parts of the image while preserving the texture in the original image in greater detail and eliminating the step effect produced by other models such as the Perona-Malik (PM) and Rudin-Osher-Fatemi (ROF) models. By comparing with other algorithms, the image edge detection effect is measured with the help of evaluation metrics such as peak signal-to-noise ratio and structural similarity, and the optimal value is selected iteratively so that the image with the best edge detection result is retained. A convolutional mask image edge detection model based on adaptive fractional-order calculus is proposed for the scattered noise in medical images. The adaption is mainly reflected in the model algorithm by constructing an exponential parameter relation that is closely related to the image, which can dynamically adjust the parameter values, thus making the model algorithm more practical. The model achieves the scattering noise removal in four steps.

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Section: Advances In Mathematical Physicsmentioning

confidence: 99%

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Chen

2021

Advances in Mathematical Physics

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“…In [20], a MapReduce (MR) approach to perform edge detection of satellite images using a nature-inspired algorithm Artificial Bee Colony (ABC) is proposed. The study compares two edge detection approaches on Hadoop with respect to scalability parameters such as scaleup and speedup.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Ant Colony Optimization in Edge Detection

Chen

2021

Preprint

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With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greaterproblems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvementsin speedup, scaleup and sizeup compared to the standard algorithms.

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“…The use of cloud-computing has also gained a greater interest in the last few years (Hedaia et al, 2020). Sharma et al (2020) have proposed a "MapReduce (MR)" approach for performing edge-detection of satellite-images by the use of the nature-inspired "Artificial Bee Colony" algorithm. Deena et al (2020) have used "natural language processing (NLP)" techniques to generate the multi-choice questions dynamically.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

Mishra¹,

Satapathy

Jain³

2022

International Journal of Service Science, Management, Engineering, and Technology

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In the present study, 13 anthropometric hand dimensions, hand grip strength, push strength, and pull strength of 90 male farmers of Odisha in India were statistically analyzed and then, fuzzy logic toolbox of MATLAB version 2010 was used in order to create the fuzzy inference system (FIS) using ANFIS. The mean hand grip strength with standard deviation was found to be 255.21 N ± 75.46. The average push strength in standing posture for farmers was found to be 193.12 N ± 76.12, whereas pull strength in standing posture was 200.59 N ± 64.02. Very high correlation coefficient i.e. 0.977, 0.994 and 0.990 was obtained between “hand length and hand grip strength”, “hand breadth with thumb and push strength”, and “hand length and pull strength”, respectively. Finally, from the obtained ANFIS models for the prediction of muscle strength, it was concluded that ANFIS could well predict the farmers muscle strength with minimum errors. This will help to evaluate muscle capabilities to avoid musculoskeletal disorders, and in ergonomic design of tools and equipment as a health-care initiative.

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Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

Cited by 7 publications

References 18 publications

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

A Distributed Ant Colony Optimization in Edge Detection

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

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Distributed Approach to Process Satellite Image Edge Detection on Hadoop Using Artificial Bee Colony

Cited by 7 publications

References 18 publications

Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection

Fractional-Order Adaptive P -Laplace Equation-Based Art Image Edge Detection

A Distributed Ant Colony Optimization in Edge Detection

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

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Product

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Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection

Fractional-Order Adaptive $P$ -Laplace Equation-Based Art Image Edge Detection